Smaller Trading Strategies May Be More Volatile
Smaller Trading Strategies May Be More Volatile
By:Kai Zeng
Smaller options positions don't always lower risk, plus delta selection and spread width affect volatility
- Position volatility is a crucial metric for evaluating portfolio performance.
- Small or seemingly less risky strategies can sometimes produce higher volatility relative to their position size.
- Risk-defined positions with extremely tight wings can lead to disproportionately high volatility as a percentage of capital used, introducing unnecessary risk.
Traders frequently analyze key statistics like average profit and loss statements (P/L), win rate and losses for various strategies. Yet, an often underappreciated metric is P/L volatility, which can provide insight into stability and risk.
Consider the performance of 10𝜟 Strangles vs. 20𝜟 strangles, both with 45 days to expiration (DTE) and exited at 21 DTE.
As expected, the 10𝜟 strangles with smaller deltas showed lower P/L but achieved a higher win rate, smaller losses and reduced P/L volatility.
However, when assessing P/L volatility as a percentage of the initial credit, the smaller delta strategy appeared more volatile because of lower premiums. This suggests a strategy perceived as "cheaper" or "safer" might not necessarily lead to reduced P/L or portfolio volatility.
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Next, let's examine SPY Put Spreads with varying wings: 30𝜟/25𝜟, 30𝜟/15𝜟, and 30𝜟/5𝜟, all exited at 21 DTE.
We found riskier strategies with wider wings resulted in higher average P/L and win rates but exhibited lower volatility relative to the credit received.
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Conversely, tighter wing spreads, perceived as less risky, showed greater position volatility. This indicates enlarging single positions moderately can help mitigate portfolio risk by reducing volatility.
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Kai Zeng, director of the research team and head of Chinese content at tastylive, has 20 years of experience in markets and derivatives trading. He cohosts several live shows, including From Theory to Practice and Building Blocks. @kai_zeng1
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